In this practical you will be answering a research question or solving a problem. For that you will create a pipeline for classification or clustering.
All the data is processed and can be found on the github repository.
Here are some proposed research questions:
You can come up with your own research question using any dataset on text analysis, e.g. from:
# path to the data
path_data = "./data/"
# How to read data (We cleaned it for you)
# data_rq1_fake = pd.read_csv(f"{path_data}/rq1_fake_news.csv.gzip",sep="\t",compression="gzip")
# data_rq1_hate_speech = pd.read_csv(f"{path_data}/rq1_hate_speech.csv.gzip",sep="\t",compression="gzip")
# data_rq1_youtube = pd.read_csv(f"{path_data}/rq1_youtube.csv.gzip",sep="\t",compression="gzip")
# data_rq2_3 = pd.read_csv(f"{path_data}/rq2_3_wiki_movie_plots.csv.gzip",sep="\t",compression="gzip")
# data_rq4 = pd.read_csv(f"{path_data}/rq4_gne-release-v1.0.csv.gzip",sep="\t",compression="gzip")
# data_rq1_fake.shape, data_rq1_hate_speech.shape, data_rq1_youtube.shape, data_rq2_3.shape, data_rq4.shape
# Data wrangling
import pandas as pd
import numpy as np
# Machine learning tools
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.linear_model import LogisticRegression
# Interpretable AI
#!pip install lime
from lime.lime_text import LimeTextExplainer
We provide code for the first dataset. Your goal is to (1) improve the classifier by using a more advanced method (2)
Data: Dataset of hate speech annotated on Internet forum posts in English at sentence-level. The source forum in Stormfront, a large online community of white nacionalists. A total of 10,568 sentence have been been extracted from Stormfront and classified as conveying hate speech or not
df = pd.read_csv(f"{path_data}/rq1_hate_speech.csv.gzip",sep="\t",compression="gzip", index_col=0)
df["label"] = df["label"].map({"hate": 1, "noHate": 0})
df = df[["text","label"]]
df = df.dropna()
print(df.shape)
df.head()
(10703, 2)
| text | label | |
|---|---|---|
| file_id | ||
| 12834217_1 | As of March 13th , 2014 , the booklet had been... | 0.0 |
| 12834217_2 | In order to help increase the booklets downloa... | 0.0 |
| 12834217_3 | ( Simply copy and paste the following text int... | 0.0 |
| 12834217_4 | Click below for a FREE download of a colorfull... | 1.0 |
| 12834217_5 | Click on the `` DOWNLOAD ( 7.42 MB ) '' green ... | 0.0 |
# # read data
# df = pd.read_csv(f"{path_data}/rq1_fake_news.csv.gzip",sep="\t",compression="gzip", index_col=0)
# df.rename(columns={"title": "text", "Fake":"label"})
# # descriptive stats
# df.groupby("Fake").count()
# split into train and test
X_train, X_test, y_train, y_test = train_test_split(df["text"].values, df["label"].values, test_size=0.33, random_state=42)
Create a pipeline that vectorizes the text and transform it using TF-IDF, and classifies the news titles using LogisticRegression.
# Pipeline
pipe = Pipeline([
('vectorizer', TfidfVectorizer(stop_words='english', #remove stopwords
lowercase=True, #convert to lowercase
token_pattern=r'(?u)\b[A-Za-z][A-Za-z]+\b')), #tokens of at least 2 characters
('clf', LogisticRegression(max_iter=10000, dual=False, solver="saga")) #logistic regression
])
# Parameters to hyptertune
param_grid = dict(vectorizer__ngram_range=[(1,1), (1,2), (1,3)], # creation of n-grams
vectorizer__min_df=[1, 10, 100], # minimum support for words
clf__C=[0.1, 1, 10, 100], # regularization
clf__penalty=["l2","l1"]) # type of regularization
# Run a grid search using cross-validation to find the best parameters
grid_search = GridSearchCV(pipe, param_grid=param_grid, verbose=True, n_jobs=-1)
# to speed it up we find the hyperparameters using a sample, and fit on the entire datast later
grid_search.fit(X_train[:1000], y_train[:1000])
# best parameters, score and estimator
print(grid_search.best_params_)
print(grid_search.best_score_)
Fitting 5 folds for each of 72 candidates, totalling 360 fits
{'clf__C': 10, 'clf__penalty': 'l2', 'vectorizer__min_df': 1, 'vectorizer__ngram_range': (1, 2)}
0.893
/Users/garci061/miniforge3/envs/st/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning:
120 fits failed out of a total of 360.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.
Below are more details about the failures:
--------------------------------------------------------------------------------
120 fits failed with the following error:
Traceback (most recent call last):
File "/Users/garci061/miniforge3/envs/st/lib/python3.10/site-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "/Users/garci061/miniforge3/envs/st/lib/python3.10/site-packages/sklearn/pipeline.py", line 390, in fit
Xt = self._fit(X, y, **fit_params_steps)
File "/Users/garci061/miniforge3/envs/st/lib/python3.10/site-packages/sklearn/pipeline.py", line 348, in _fit
X, fitted_transformer = fit_transform_one_cached(
File "/Users/garci061/miniforge3/envs/st/lib/python3.10/site-packages/joblib/memory.py", line 349, in __call__
return self.func(*args, **kwargs)
File "/Users/garci061/miniforge3/envs/st/lib/python3.10/site-packages/sklearn/pipeline.py", line 893, in _fit_transform_one
res = transformer.fit_transform(X, y, **fit_params)
File "/Users/garci061/miniforge3/envs/st/lib/python3.10/site-packages/sklearn/feature_extraction/text.py", line 2077, in fit_transform
X = super().fit_transform(raw_documents)
File "/Users/garci061/miniforge3/envs/st/lib/python3.10/site-packages/sklearn/feature_extraction/text.py", line 1347, in fit_transform
X, self.stop_words_ = self._limit_features(
File "/Users/garci061/miniforge3/envs/st/lib/python3.10/site-packages/sklearn/feature_extraction/text.py", line 1179, in _limit_features
raise ValueError(
ValueError: After pruning, no terms remain. Try a lower min_df or a higher max_df.
warnings.warn(some_fits_failed_message, FitFailedWarning)
/Users/garci061/miniforge3/envs/st/lib/python3.10/site-packages/sklearn/model_selection/_search.py:969: UserWarning: One or more of the test scores are non-finite: [0.892 0.892 0.892 0.892 0.892 0.892 nan nan nan 0.892 0.892 0.892
0.892 0.892 0.892 nan nan nan 0.892 0.892 0.892 0.891 0.891 0.891
nan nan nan 0.889 0.892 0.892 0.889 0.889 0.889 nan nan nan
0.891 0.893 0.891 0.884 0.884 0.884 nan nan nan 0.882 0.866 0.851
0.883 0.883 0.883 nan nan nan 0.885 0.891 0.892 0.882 0.882 0.882
nan nan nan 0.88 0.873 0.861 0.88 0.88 0.88 nan nan nan]
warnings.warn(
# print resutls
results = pd.DataFrame(grid_search.cv_results_)
results.sort_values(by="mean_test_score", ascending=False).head(10)
| mean_fit_time | std_fit_time | mean_score_time | std_score_time | param_clf__C | param_clf__penalty | param_vectorizer__min_df | param_vectorizer__ngram_range | params | split0_test_score | split1_test_score | split2_test_score | split3_test_score | split4_test_score | mean_test_score | std_test_score | rank_test_score | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 37 | 0.076167 | 0.002831 | 0.003030 | 0.001542 | 10 | l2 | 1 | (1, 2) | {'clf__C': 10, 'clf__penalty': 'l2', 'vectoriz... | 0.895 | 0.900 | 0.895 | 0.885 | 0.89 | 0.893 | 0.005099 | 1 |
| 0 | 0.035690 | 0.004285 | 0.002435 | 0.000704 | 0.1 | l2 | 1 | (1, 1) | {'clf__C': 0.1, 'clf__penalty': 'l2', 'vectori... | 0.895 | 0.895 | 0.890 | 0.890 | 0.89 | 0.892 | 0.002449 | 2 |
| 1 | 0.057556 | 0.004545 | 0.004739 | 0.001589 | 0.1 | l2 | 1 | (1, 2) | {'clf__C': 0.1, 'clf__penalty': 'l2', 'vectori... | 0.895 | 0.895 | 0.890 | 0.890 | 0.89 | 0.892 | 0.002449 | 2 |
| 28 | 0.098490 | 0.021747 | 0.004665 | 0.003109 | 1 | l1 | 1 | (1, 2) | {'clf__C': 1, 'clf__penalty': 'l1', 'vectorize... | 0.895 | 0.895 | 0.890 | 0.890 | 0.89 | 0.892 | 0.002449 | 2 |
| 56 | 0.174241 | 0.005164 | 0.004954 | 0.002020 | 100 | l2 | 1 | (1, 3) | {'clf__C': 100, 'clf__penalty': 'l2', 'vectori... | 0.895 | 0.885 | 0.905 | 0.885 | 0.89 | 0.892 | 0.007483 | 2 |
| 20 | 0.077374 | 0.010531 | 0.005199 | 0.003462 | 1 | l2 | 1 | (1, 3) | {'clf__C': 1, 'clf__penalty': 'l2', 'vectorize... | 0.895 | 0.895 | 0.890 | 0.890 | 0.89 | 0.892 | 0.002449 | 2 |
| 19 | 0.059178 | 0.007435 | 0.004714 | 0.002201 | 1 | l2 | 1 | (1, 2) | {'clf__C': 1, 'clf__penalty': 'l2', 'vectorize... | 0.895 | 0.895 | 0.890 | 0.890 | 0.89 | 0.892 | 0.002449 | 2 |
| 18 | 0.028727 | 0.002456 | 0.003007 | 0.001460 | 1 | l2 | 1 | (1, 1) | {'clf__C': 1, 'clf__penalty': 'l2', 'vectorize... | 0.895 | 0.895 | 0.890 | 0.890 | 0.89 | 0.892 | 0.002449 | 2 |
| 14 | 0.017252 | 0.005573 | 0.004315 | 0.003970 | 0.1 | l1 | 10 | (1, 3) | {'clf__C': 0.1, 'clf__penalty': 'l1', 'vectori... | 0.895 | 0.895 | 0.890 | 0.890 | 0.89 | 0.892 | 0.002449 | 2 |
| 13 | 0.015535 | 0.004409 | 0.002851 | 0.001719 | 0.1 | l1 | 10 | (1, 2) | {'clf__C': 0.1, 'clf__penalty': 'l1', 'vectori... | 0.895 | 0.895 | 0.890 | 0.890 | 0.89 | 0.892 | 0.002449 | 2 |
# Use the best parameters in the pipe and fit with the entire dataset
pipe = pipe.set_params(**grid_search.best_params_)
clf_best = pipe.fit(X_train, y_train)
# print vocabulary size
print(len(clf_best["vectorizer"].get_feature_names_out()))
#vocabulary
#clf_best["vectorizer"].vocabulary_
# the best score achieved
print(clf_best.score(X_train, y_train))
# the best score achieved
print(clf_best.score(X_test, y_test))
53376 0.9993027471761261 0.8958097395243488
# Add predicitons to dataframe
df["predicted"] = clf_best.predict(df["text"])
df["predicted_prob_fake"] = clf_best.predict_proba(df["text"])[:,1]
df
| text | label | predicted | predicted_prob_fake | |
|---|---|---|---|---|
| file_id | ||||
| 12834217_1 | As of March 13th , 2014 , the booklet had been... | 0.0 | 0.0 | 0.017513 |
| 12834217_2 | In order to help increase the booklets downloa... | 0.0 | 0.0 | 0.018836 |
| 12834217_3 | ( Simply copy and paste the following text int... | 0.0 | 0.0 | 0.012749 |
| 12834217_4 | Click below for a FREE download of a colorfull... | 1.0 | 1.0 | 0.692457 |
| 12834217_5 | Click on the `` DOWNLOAD ( 7.42 MB ) '' green ... | 0.0 | 0.0 | 0.016740 |
| ... | ... | ... | ... | ... |
| 33676864_5 | Billy - `` That guy would n't leave me alone ,... | 0.0 | 0.0 | 0.057341 |
| 33677019_1 | Wish we at least had a Marine Le Pen to vote f... | 0.0 | 0.0 | 0.048650 |
| 33677019_2 | Its like the choices are white genocide candid... | 0.0 | 0.0 | 0.040065 |
| 33677053_1 | Why White people used to say that sex was a si... | 1.0 | 0.0 | 0.112943 |
| 33677053_2 | Now I get it ! | 0.0 | 0.0 | 0.042926 |
10703 rows × 4 columns
We can use the coefficients of the Logistic regression
# Extract the coeficients from the omdel
coefs = pd.DataFrame([clf_best["vectorizer"].get_feature_names_out(),
clf_best["clf"].coef_[0]]).T
coefs.columns = ["gram","coef"]
# top words influencing fake
display(coefs.sort_values(by="coef", ascending=False).head(10))
# top words influencing true
display(coefs.sort_values(by="coef", ascending=True).head(10))
| gram | coef | |
|---|---|---|
| 31434 | negroes | 8.126356 |
| 4218 | black | 7.268875 |
| 26064 | liberals | 6.232499 |
| 18671 | groid | 6.174792 |
| 15506 | filth | 6.048523 |
| 40573 | scum | 6.042985 |
| 1723 | ape | 5.95158 |
| 1732 | apes | 4.900917 |
| 686 | africa | 4.817527 |
| 30570 | mud | 4.78217 |
| gram | coef | |
|---|---|---|
| 53076 | youtube | -3.435731 |
| 39652 | said | -2.728514 |
| 52725 | year | -2.448316 |
| 46099 | thanks | -2.222029 |
| 46670 | thread | -2.207491 |
| 30729 | music | -2.145789 |
| 27704 | lot | -2.082198 |
| 8422 | comes | -2.038594 |
| 19172 | hair | -2.015077 |
| 30983 | nationalist | -1.957227 |
# Find some extreme examples
less_fake = df.sort_values(by="predicted_prob_fake").head(1).values[0][0]
most_fake = df.sort_values(by="predicted_prob_fake").tail(1).values[0][0]
df_confused = df.loc[df["label"] != df["predicted"]]
pred_fake_not_fake = df_confused.loc[df_confused["label"]==0].sort_values(by="predicted_prob_fake").tail(1).values[0][0]
pred_not_fake_fake = df_confused.loc[df_confused["label"]==1].sort_values(by="predicted_prob_fake").head(1).values[0][0]
pred_50_50 = "She says the class is out of control and the kids are unteachable , and the black administration does not support her "
print("Least hate: ", less_fake)
print("Most hate: ", most_fake)
print("Predicted very hate but not hateful: ", pred_fake_not_fake)
print("Predicted very innocuous but hateful: ", pred_not_fake_fake)
print("Predicted 50/50: ", pred_50_50)
Least hate: - YouTube Most hate: Look what happens when Whites leave black Countries alone to do what they do naturally The blacks in White Countries today should be on their knees thanking Whites for trying to civilize them Predicted very hate but not hateful: Too many whites think they deserve what negroes dish out because of guilt . Predicted very innocuous but hateful: https://www.stormfront.org/forum/t1020784/ https : //www.stormfront.org/forum/t102 ... ghlight = sweden https : //www.stormfront.org/forum/t102 ... ghlight = sweden https : //www.stormfront.org/forum/t101 ... ghlight = sweden https : //www.stormfront.org/forum/t101 ... ghlight = sweden https : //www.stormfront.org/forum/t100 ... ghlight = sweden https : //www.stormfront.org/forum/t100 ... ghlight = sweden https : //www.stormfront.org/forum/t100 ... ghlight = sweden God save them ..... Predicted 50/50: She says the class is out of control and the kids are unteachable , and the black administration does not support her
# start the explainer
explainer = LimeTextExplainer(class_names = ["Innocuous", "Hateful"], bow=False)
# shows the explanation for our example instances
for text in [less_fake, most_fake, pred_fake_not_fake, pred_not_fake_fake, pred_50_50]:
exp = explainer.explain_instance(text,
clf_best.predict_proba,
num_features = 10,
num_samples = 1000)
exp.show_in_notebook(text=text)
print(exp.as_list())
print("-"*100)
[('YouTube', -0.008323447828899103)]
----------------------------------------------------------------------------------------------------
[('black', 0.1530117030774071), ('leave', 0.09460229211793703), ('Whites', 0.08440702391812448), ('blacks', 0.06747991458238094), ('Whites', 0.0663042768418059), ('today', -0.056455629784203834), ('knees', -0.05531198997969092), ('Countries', 0.05215418416881412), ('Countries', 0.04109886367418432), ('happens', 0.025597988325753687)]
----------------------------------------------------------------------------------------------------
[('negroes', 0.5153545730863675), ('whites', 0.16865803264257337), ('guilt', 0.04260701393581915), ('think', -0.020238036445078867), ('many', -0.014668719004717558), ('because', 0.011515653045518066), ('out', 0.008668108104411673), ('Too', -0.00800020931931476), ('they', -0.006445943447796432), ('deserve', 0.005050840088067402)]
----------------------------------------------------------------------------------------------------
[('www', -0.0034857808931292185), ('www', -0.0032015608771622565), ('www', -0.0028891902361937527), ('sweden', 0.0017379058465679586), ('sweden', 0.0013248169330744022), ('sweden', 0.0013241872009841257), ('sweden', 0.0012905366943266289), ('sweden', 0.0011741381828446085), ('sweden', 0.0009545469616767627), ('sweden', 0.0003763950699854398)]
----------------------------------------------------------------------------------------------------
[('black', 0.4121239839237728), ('control', 0.17150454924079028), ('administration', -0.12989071474318323), ('class', -0.08985109690318371), ('kids', -0.057534661043636165), ('does', -0.04198309615168351), ('says', 0.022866222436935202), ('support', 0.01404376744025586), ('the', 0.0062408285781966775), ('unteachable', 0.00552002712867932)]
----------------------------------------------------------------------------------------------------
exp = explainer.explain_instance("I believe Dutch people have inferior food and they should be colonized by Belgium",
clf_best.predict_proba,
num_features = 10,
num_samples = 1000)
exp.show_in_notebook(text=text)
print(exp.as_list())
print("-"*100)
[('people', -0.009226299460579325), ('food', 0.008532091914201272), ('inferior', 0.007381136867985198), ('Belgium', -0.0032930331397772604), ('believe', -0.001058917896783231), ('they', 0.0001833517378424935), ('by', -0.00013794883401532004), ('and', 0.00013238985184920658), ('I', -0.00011831954157026918), ('Dutch', -0.00010376687225336656)]
----------------------------------------------------------------------------------------------------
Either:
#!pip install scikeras
from scikeras.wrappers import KerasClassifier
#from keras.wrappers.scikit_learn import KerasClassifier
from keras_preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.models import Sequential
from keras import layers, utils
def plot_history(history, val=0):
acc = history['accuracy']
if val == 1:
val_acc = history['val_accuracy'] # we can add a validation set in our fit function with nn
loss = history['loss']
if val == 1:
val_loss = history['val_loss']
x = range(1, len(acc) + 1)
plt.figure(figsize=(12, 5))
plt.subplot(1, 2, 1)
plt.plot(x, acc, 'b', label='Training accuracy')
if val == 1:
plt.plot(x, val_acc, 'r', label='Validation accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.title('Accuracy')
plt.legend()
plt.subplot(1, 2, 2)
plt.plot(x, loss, 'b', label='Training loss')
if val == 1:
plt.plot(x, val_loss, 'r', label='Validation loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.title('Loss')
plt.legend()
## CREATE MODEL
def create_model(num_filters=64, kernel_size=3, embedding_dim=50, maxlen=100, num_classes=2):
model = Sequential()
model.add(layers.Embedding(vocab_size, embedding_dim, input_length=maxlen))
model.add(layers.Conv1D(num_filters, kernel_size, activation='relu'))
model.add(layers.GlobalMaxPooling1D())
model.add(layers.Dense(10, activation='relu'))
model.add(layers.Dense(num_classes, activation='sigmoid'))
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
return model
## CLASS FOR PREPROCESSING (needed to work with pipelines)
class preprocessing():
def __init__(self, num_words=20000, maxlen=100):
self.maxlen = maxlen
self.tokenizer = Tokenizer(num_words=num_words)
def fit(self, X, y=None):
self.tokenizer.fit_on_texts(X)
return self
def transform(self, X, y=None):
X_ = self.tokenizer.texts_to_sequences(X)
return pad_sequences(X_, padding='post', maxlen=self.maxlen)
## PROCESS DATA
X_train, X_test, y_train, y_test = train_test_split(df["text"].values, df["label"].values, test_size=0.33, random_state=42)
# Encode the list of newsgroups into categorical integer values
y_train = utils.to_categorical(y_train)
y_test = utils.to_categorical(y_test)
## CREATE PIPELINE
# Use the best parameters in the pipe and fit with the entire dataset
pipe_preproc = Pipeline([
("preproc", preprocessing())])
pipe_est = Pipeline([
('clf', KerasClassifier(model=create_model,
epochs = 10,
batch_size=64,
verbose=True,
num_filters=32 )) #logistic regression
])
pipe_preproc.fit(X_train)
X_train_p = pipe_preproc.transform(X_train)
X_test_p = pipe_preproc.transform(X_test)
vocab_size = len(pipe_preproc["preproc"].tokenizer.word_index) + 1
print(vocab_size)
# test it works
pipe_est.fit(X_train_p[:500], y_train[:500])
12771 Metal device set to: Apple M1
2022-07-28 11:29:52.668947: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support. 2022-07-28 11:29:52.669244: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)
Epoch 1/10
2022-07-28 11:29:53.012129: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz 2022-07-28 11:29:53.273335: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
8/8 [==============================] - 2s 38ms/step - loss: 0.6804 - accuracy: 0.8100 Epoch 2/10 8/8 [==============================] - 0s 31ms/step - loss: 0.6414 - accuracy: 0.9020 Epoch 3/10 8/8 [==============================] - 0s 30ms/step - loss: 0.5806 - accuracy: 0.9020 Epoch 4/10 8/8 [==============================] - 0s 33ms/step - loss: 0.5087 - accuracy: 0.9020 Epoch 5/10 8/8 [==============================] - 0s 30ms/step - loss: 0.4325 - accuracy: 0.9020 Epoch 6/10 8/8 [==============================] - 0s 28ms/step - loss: 0.3621 - accuracy: 0.9020 Epoch 7/10 8/8 [==============================] - 0s 32ms/step - loss: 0.3229 - accuracy: 0.9020 Epoch 8/10 8/8 [==============================] - 0s 27ms/step - loss: 0.3084 - accuracy: 0.9020 Epoch 9/10 8/8 [==============================] - 0s 28ms/step - loss: 0.3042 - accuracy: 0.9020 Epoch 10/10 8/8 [==============================] - 0s 29ms/step - loss: 0.2971 - accuracy: 0.9020
Pipeline(steps=[('clf',
KerasClassifier(batch_size=64, epochs=10, model=<function create_model at 0x15322edd0>, num_filters=32, verbose=True))])
pipe_est["clf"].model_.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding (Embedding) (None, 100, 50) 638550
conv1d (Conv1D) (None, 98, 32) 4832
global_max_pooling1d (Globa (None, 32) 0
lMaxPooling1D)
dense (Dense) (None, 10) 330
dense_1 (Dense) (None, 2) 22
=================================================================
Total params: 643,734
Trainable params: 643,734
Non-trainable params: 0
_________________________________________________________________
# I'm having some Apple M1 problems (warnings that are not useful).
# The code below disables those warnings (usually not a good idea)
#import os
# os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
## HYPERPARAMETER TUNING
param_grid = dict(clf__model__num_filters=[32, 64, 128],
clf__model__kernel_size=[3, 5, 7],
clf__model__embedding_dim=[50, 100],
clf__verbose=[False])
grid = RandomizedSearchCV(estimator=pipe_est,
param_distributions=param_grid,
cv=5,
n_jobs=-1,
verbose=True,
n_iter=10)
grid.fit(X_train_p[:1000], y_train[:1000])
Fitting 5 folds for each of 10 candidates, totalling 50 fits Metal device set to: Apple M1 Metal device set to: Apple M1 Metal device set to: Apple M1 Metal device set to: Apple M1 Metal device set to: Apple M1 Metal device set to: Apple M1 Metal device set to: Apple M1
2022-07-28 11:31:45.847851: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support. 2022-07-28 11:31:45.847984: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>) 2022-07-28 11:31:45.848731: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support. 2022-07-28 11:31:45.848844: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>) 2022-07-28 11:31:45.849644: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support. 2022-07-28 11:31:45.849753: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>) 2022-07-28 11:31:45.849899: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support. 2022-07-28 11:31:45.850001: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>) 2022-07-28 11:31:45.852124: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support. 2022-07-28 11:31:45.852283: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>) 2022-07-28 11:31:45.855491: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support. 2022-07-28 11:31:45.855942: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>) 2022-07-28 11:31:45.856140: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support. 2022-07-28 11:31:45.856443: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>) 2022-07-28 11:31:46.060452: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support. 2022-07-28 11:31:46.060765: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)
Metal device set to: Apple M1
2022-07-28 11:31:46.335480: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz 2022-07-28 11:31:46.347713: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz 2022-07-28 11:31:46.350372: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz 2022-07-28 11:31:46.356936: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz 2022-07-28 11:31:46.368465: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz 2022-07-28 11:31:46.369808: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz 2022-07-28 11:31:46.390346: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz 2022-07-28 11:31:46.842067: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:31:46.874273: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:31:47.053004: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz 2022-07-28 11:31:47.106584: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:31:47.107602: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:31:47.119101: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:31:47.138170: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:31:47.158375: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:31:47.585681: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:02.126720: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
systemMemory: 16.00 GB maxCacheSize: 5.33 GB
2022-07-28 11:32:02.670353: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:02.939675: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. /Users/garci061/miniforge3/envs/st/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py:702: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak. warnings.warn(
systemMemory: 16.00 GB maxCacheSize: 5.33 GB systemMemory: 16.00 GB maxCacheSize: 5.33 GB Metal device set to: Apple M1 Metal device set to: Apple M1
2022-07-28 11:32:06.509078: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support. 2022-07-28 11:32:06.509249: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>) 2022-07-28 11:32:06.509310: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support. 2022-07-28 11:32:06.509400: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>) 2022-07-28 11:32:06.708156: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz 2022-07-28 11:32:06.740900: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz 2022-07-28 11:32:06.757793: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support. 2022-07-28 11:32:06.757918: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)
Metal device set to: Apple M1
2022-07-28 11:32:06.993914: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz 2022-07-28 11:32:07.055541: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:07.064827: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:07.393606: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:08.217012: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
systemMemory: 16.00 GB maxCacheSize: 5.33 GB
2022-07-28 11:32:09.303660: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
systemMemory: 16.00 GB maxCacheSize: 5.33 GB
2022-07-28 11:32:11.398919: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:12.033015: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:12.161756: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
systemMemory: 16.00 GB maxCacheSize: 5.33 GB Metal device set to: Apple M1
2022-07-28 11:32:12.674039: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support. 2022-07-28 11:32:12.674622: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>) 2022-07-28 11:32:12.948020: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
systemMemory: 16.00 GB maxCacheSize: 5.33 GB
2022-07-28 11:32:13.400999: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
systemMemory: 16.00 GB maxCacheSize: 5.33 GB Metal device set to: Apple M1
2022-07-28 11:32:14.186092: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support. 2022-07-28 11:32:14.186227: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>) 2022-07-28 11:32:14.419735: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz 2022-07-28 11:32:14.734624: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:15.943686: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
Metal device set to: Apple M1
2022-07-28 11:32:16.577229: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support. 2022-07-28 11:32:16.579177: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>) 2022-07-28 11:32:17.301679: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz 2022-07-28 11:32:17.435286: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:17.708619: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support. 2022-07-28 11:32:17.709237: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>) 2022-07-28 11:32:17.880538: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
Metal device set to: Apple M1
2022-07-28 11:32:18.058146: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
Metal device set to: Apple M1
2022-07-28 11:32:18.397020: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support. 2022-07-28 11:32:18.397129: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>) 2022-07-28 11:32:18.426165: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:18.709174: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz 2022-07-28 11:32:19.024930: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:24.018898: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:24.440536: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:24.534981: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:25.655891: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:25.837065: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:25.841337: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:29.367721: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:29.836115: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:33.166459: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:33.679122: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:33.875230: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:34.340003: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:36.487021: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:36.905177: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:38.371717: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:38.799789: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:39.146589: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:39.563851: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:41.156706: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:41.677873: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:44.928712: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:45.158643: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:45.383390: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:45.737218: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:47.242610: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:47.878276: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:48.120971: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:48.625425: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:53.768239: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:54.264958: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:56.359200: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:56.896828: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:57.268512: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:32:58.435680: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:33:01.826507: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:33:02.956068: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:33:04.055038: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:33:04.620879: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:33:05.669251: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:33:06.276722: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:33:12.867255: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:33:13.304173: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:33:13.468903: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:33:13.834674: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:33:13.933746: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:33:14.573999: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:33:17.492334: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:33:18.025816: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:33:19.951920: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:33:20.256229: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:33:20.920109: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:33:21.062041: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:33:21.386512: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:33:22.672253: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:33:24.557671: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:33:25.100871: I 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tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 2022-07-28 11:33:49.848658: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
RandomizedSearchCV(cv=5,
estimator=Pipeline(steps=[('clf',
KerasClassifier(batch_size=64, epochs=10, model=<function create_model at 0x15322edd0>, num_filters=32, verbose=True))]),
n_jobs=-1,
param_distributions={'clf__model__embedding_dim': [50, 100],
'clf__model__kernel_size': [3, 5, 7],
'clf__model__num_filters': [32, 64,
128],
'clf__verbose': [False]},
verbose=True)
print(grid.best_score_)
print(grid.best_params_)
0.892
{'clf__verbose': False, 'clf__model__num_filters': 32, 'clf__model__kernel_size': 3, 'clf__model__embedding_dim': 100}
# Use the best parameters in the pipe and fit with the entire dataset
clf_best = grid.best_estimator_
clf_best = pipe_est.fit(X_train_p, y_train,
clf__validation_data=(X_test_p, y_test))
Epoch 1/10 1/113 [..............................] - ETA: 1:19 - loss: 0.6961 - accuracy: 0.3594
2022-07-28 11:33:56.102679: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
113/113 [==============================] - ETA: 0s - loss: 0.4541 - accuracy: 0.8780
2022-07-28 11:34:00.328055: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
113/113 [==============================] - 5s 39ms/step - loss: 0.4541 - accuracy: 0.8780 - val_loss: 0.3386 - val_accuracy: 0.8933 Epoch 2/10 113/113 [==============================] - 4s 32ms/step - loss: 0.3388 - accuracy: 0.8858 - val_loss: 0.3082 - val_accuracy: 0.8933 Epoch 3/10 113/113 [==============================] - 3s 30ms/step - loss: 0.2478 - accuracy: 0.8939 - val_loss: 0.2470 - val_accuracy: 0.9077 Epoch 4/10 113/113 [==============================] - 3s 30ms/step - loss: 0.1348 - accuracy: 0.9511 - val_loss: 0.2474 - val_accuracy: 0.9080 Epoch 5/10 113/113 [==============================] - 3s 31ms/step - loss: 0.0646 - accuracy: 0.9810 - val_loss: 0.2752 - val_accuracy: 0.9043 Epoch 6/10 113/113 [==============================] - 4s 31ms/step - loss: 0.0303 - accuracy: 0.9940 - val_loss: 0.3128 - val_accuracy: 0.9049 Epoch 7/10 113/113 [==============================] - 4s 31ms/step - loss: 0.0147 - accuracy: 0.9980 - val_loss: 0.3420 - val_accuracy: 0.9032 Epoch 8/10 113/113 [==============================] - 3s 31ms/step - loss: 0.0075 - accuracy: 0.9993 - val_loss: 0.3848 - val_accuracy: 0.8998 Epoch 9/10 113/113 [==============================] - 3s 30ms/step - loss: 0.0035 - accuracy: 0.9996 - val_loss: 0.4301 - val_accuracy: 0.9052 Epoch 10/10 113/113 [==============================] - 3s 31ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.4402 - val_accuracy: 0.8989
import pylab as plt
plot_history(clf_best["clf"].history_, val=True)
# Use the best epochs
clf_best = grid.best_estimator_
clf_best = pipe_est.fit(X_train_p, y_train,
clf__validation_data=(X_test_p, y_test),
clf__epochs=4)
Epoch 1/4 1/113 [..............................] - ETA: 59s - loss: 0.6953 - accuracy: 0.3281
2022-07-28 11:34:32.792323: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
113/113 [==============================] - ETA: 0s - loss: 0.4559 - accuracy: 0.8778
2022-07-28 11:34:36.634564: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
113/113 [==============================] - 4s 35ms/step - loss: 0.4559 - accuracy: 0.8778 - val_loss: 0.3385 - val_accuracy: 0.8933 Epoch 2/4 113/113 [==============================] - 4s 31ms/step - loss: 0.3390 - accuracy: 0.8858 - val_loss: 0.3052 - val_accuracy: 0.8933 Epoch 3/4 113/113 [==============================] - 3s 31ms/step - loss: 0.2508 - accuracy: 0.8944 - val_loss: 0.2471 - val_accuracy: 0.9029 Epoch 4/4 113/113 [==============================] - 3s 31ms/step - loss: 0.1373 - accuracy: 0.9449 - val_loss: 0.2531 - val_accuracy: 0.9066
# Find some extreme examples
less_fake = "- YouTube"
most_fake = "Look what happens when Whites leave black Countries alone to do what they do naturally The blacks in White Countries today should be on their knees thanking Whites for trying to civilize them"
pred_fake_not_fake = "Too many whites think they deserve what negroes dish out because of guilt ."
pred_not_fake_fake = "https://www.stormfront.org/forum/t1020784/ https : //www.stormfront.org/forum/t102 ... ghlight = sweden https : //www.stormfront.org/forum/t102 ... ghlight = sweden https : //www.stormfront.org/forum/t101 ... ghlight = sweden https : //www.stormfront.org/forum/t101 ... ghlight = sweden https : //www.stormfront.org/forum/t100 ... ghlight = sweden https : //www.stormfront.org/forum/t100 ... ghlight = sweden https : //www.stormfront.org/forum/t100 ... ghlight = sweden God save them ....."
pred_50_50 = "She says the class is out of control and the kids are unteachable , and the black administration does not support her "
print("Least fake: ", less_fake)
print("Most fake: ", most_fake)
print("Predicted very fake but not fake: ", pred_fake_not_fake)
print("Predicted very true but fake: ", pred_not_fake_fake)
print("Predicted 50/50: ", pred_50_50)
Least fake: - YouTube Most fake: Look what happens when Whites leave black Countries alone to do what they do naturally The blacks in White Countries today should be on their knees thanking Whites for trying to civilize them Predicted very fake but not fake: Too many whites think they deserve what negroes dish out because of guilt . Predicted very true but fake: https://www.stormfront.org/forum/t1020784/ https : //www.stormfront.org/forum/t102 ... ghlight = sweden https : //www.stormfront.org/forum/t102 ... ghlight = sweden https : //www.stormfront.org/forum/t101 ... ghlight = sweden https : //www.stormfront.org/forum/t101 ... ghlight = sweden https : //www.stormfront.org/forum/t100 ... ghlight = sweden https : //www.stormfront.org/forum/t100 ... ghlight = sweden https : //www.stormfront.org/forum/t100 ... ghlight = sweden God save them ..... Predicted 50/50: She says the class is out of control and the kids are unteachable , and the black administration does not support her
# start the explainer
explainer = LimeTextExplainer(class_names = ["Innocuous", "Hate"], bow=False)
# relying on global objects (not too nice)
def create_proba(text):
t = pipe_preproc.transform(text)
return clf_best.predict_proba(t)
# shows the explanation for our example instances
for text in [less_fake, most_fake, pred_fake_not_fake, pred_not_fake_fake, pred_50_50]:
exp = explainer.explain_instance(text,
create_proba,
num_features = 10,
num_samples = 1000)
exp.show_in_notebook(text=text)
print(exp.as_list())
print("-"*100)
16/16 [==============================] - 0s 3ms/step
2022-07-28 11:34:47.453158: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
[('YouTube', -0.006048106422461584)]
----------------------------------------------------------------------------------------------------
16/16 [==============================] - 0s 2ms/step
[('black', 0.17564516591637236), ('them', 0.1730713502056678), ('Countries', 0.1072953338390616), ('Countries', 0.09632486012554035), ('blacks', 0.09560279684615887), ('leave', 0.0928889687572245), ('they', 0.07251502736442184), ('in', -0.06562443216546078), ('do', -0.06013481985526459), ('civilize', 0.049005462434422664)]
----------------------------------------------------------------------------------------------------
16/16 [==============================] - 0s 2ms/step
[('negroes', 0.5407839683690124), ('guilt', 0.0996798238726905), ('they', 0.09083990001563409), ('whites', 0.06668847726330036), ('Too', 0.04226610782525202), ('deserve', 0.041455030277885906), ('many', 0.039044909904479536), ('think', -0.018852046466716968), ('of', 0.014071091668857698), ('out', 0.010663000654574178)]
----------------------------------------------------------------------------------------------------
16/16 [==============================] - 0s 2ms/step
[('God', 0.019535086693903983), ('them', 0.018414271795680722), ('sweden', 0.011708416297367371), ('save', 0.006463289614638867), ('org', 0.002587474461743947), ('sweden', 0.002338195193671646), ('stormfront', -0.0015784559800041808), ('stormfront', -0.001430015586189894), ('stormfront', -0.000956935802031727), ('www', -0.0009278344170942581)]
----------------------------------------------------------------------------------------------------
16/16 [==============================] - 0s 2ms/step
[('black', 0.12659400083549735), ('her', -0.07619141311656674), ('the', 0.06516765533330464), ('support', 0.04171327247029013), ('administration', -0.0394739844532431), ('does', 0.034507430097908666), ('are', 0.013991215055108124), ('and', 0.011879212101886829), ('not', -0.010455288771714415), ('class', -0.010026869999929328)]
----------------------------------------------------------------------------------------------------
exp = explainer.explain_instance("I believe Dutch people have inferior food and they should be colonized by Belgium",
create_proba,
num_features = 10,
num_samples = 1000)
exp.show_in_notebook(text=text)
print(exp.as_list())
print("-"*100)
16/16 [==============================] - 0s 2ms/step
[('they', 0.06445486105964748), ('be', 0.029905304827850636), ('Belgium', -0.010661036961754007), ('food', -0.0063249991623368085), ('inferior', 0.005178134157707648), ('I', -0.003084906335237635), ('have', -0.0030618668047911325), ('by', 0.0015078686104735845), ('colonized', 0.0014220388819078382), ('Dutch', -0.0012953540318621458)]
----------------------------------------------------------------------------------------------------